Agent Architectures Overview

Understanding different agent architectures is crucial for building effective AI systems.

ReAct (Reasoning + Acting)

ReAct combines reasoning traces with action execution. The agent alternates between:

1. Thought: Internal reasoning about the current state
2. Action: Taking an action in the environment
3. Observation: Receiving feedback from the action

# ReAct loop example
def react_agent(task):
    while not complete:
        thought = llm.think(f"Task: {task}\nHistory: {history}")
        action = llm.decide_action(thought)
        observation = environment.execute(action)
        history.append((thought, action, observation))

Plan-and-Execute

This architecture separates planning from execution:

1. Create a complete plan upfront
2. Execute each step sequentially
3. Re-plan if necessary

# Plan-and-Execute
plan = planner.create_plan(task)
for step in plan.steps:
    result = executor.run(step)
    if result.failed:
        plan = planner.replan(task, result.error)

Reflection Architecture

Agents that review and improve their own outputs:

result = agent.execute(task)
reflection = agent.reflect(result)
if reflection.needs_improvement:
    result = agent.revise(result, reflection.feedback)

Multi-Agent Systems

Multiple specialized agents working together:

Orchestrator Agent
├── Research Agent → gathers information
├── Analysis Agent → processes data
└── Writer Agent → generates output

Choosing an Architecture

Best Practices

• Start simple, add complexity as needed
• Always include error handling and recovery
• Monitor and log agent behavior for debugging
• Set clear stopping conditions to prevent infinite loops